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Snowflake DEA-C02 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Data Ingestion and Sourcing	20-25%	- Design and implement continuous and batch ingestion pipelines - Handle different data formats: structured, semi-structured, unstructured - Ingest data from various sources: data lakes, APIs, on-premises systems, cloud storage - Use Snowflake ingestion methods: Snowpipe, COPY INTO, External Functions
Topic 2: Data Governance, Security, and Compliance	10-15%	- Manage data lineage, cataloging, and compliance policies - Implement access control: RBAC, authentication, authorization - Enforce data quality and governance standards - Apply data protection: encryption, masking, row-level security
Topic 3: Data Pipeline Architecture and Design	15-20%	- Apply design patterns for data engineering workloads - Build end-to-end near real-time streaming solutions - Integrate with external tools and platforms: orchestration, BI, ML - Design scalable, reliable, and maintainable data pipelines
Topic 4: Performance Optimization and Compute Management	15-20%	- Optimize query performance: clustering, partitioning, materialized views - Use search optimization and query acceleration services - Monitor and tune workloads and resource utilization - Manage virtual warehouses: sizing, scaling, multi-cluster, cost control
Topic 5: Data Sharing and Collaboration	5-10%	- Design multi-tenant and cross-account data architectures - Implement secure data sharing and data exchanges - Work with Snowflake Data Marketplace and external data providers
Topic 6: Data Transformation and Processing	20-25%	- Manage data quality, validation, and deduplication - Transform and enrich data using SQL, JavaScript, Python, and Snowpark - Implement ELT/ETL workflows using Streams, Tasks, and Dynamic Tables - Process semi-structured data: JSON, Avro, Parquet, ORC

>> DEA-C02関連日本語内容 <<

最新のDEA-C02関連日本語内容 | 素晴らしい合格率のDEA-C02: SnowPro Advanced: Data Engineer (DEA-C02) | 完璧なDEA-C02資格準備

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Snowflake SnowPro Advanced: Data Engineer (DEA-C02) 認定 DEA-C02 試験問題 (Q133-Q138):

質問 # 133
You are using the Snowflake REST API to insert data into a table named 'RAW JSON DATA. The JSON data is complex and nested, and you want to efficiently parse and flatten it into a relational structure. You have the following JSON sample:

Which SQL statement, executed after loading the raw JSON using the REST API, is the MOST efficient way to flatten the JSON and extract relevant fields into a new table named 'PURCHASES' with columns like 'EVENT TYPE', 'USER D', 'EMAIL', 'STREET, 'CITY', 'ITEM ID', and 'PRICE'?

A. Option B
B. Option C
C. Option E
D. Option D
E. Option A

正解：B

解説：
Option C is the most efficient and correct. It uses the correct Snowflake syntax for accessing JSON elements with and for data type casting and also correctly uses the TABLE(FLATTEN()) function to handle the nested 'items' array. Option A utilizes a deprecated syntax LATERAL FLATTEN'. Options B, D and E don't handle the nested JSON structure properly or the flattening of the 'items' array, resulting in incomplete or incorrect data extraction.

質問 # 134
Consider the following scenario: You have a Snowflake task that refreshes a materialized view. The materialized view is based on a large base table that is constantly being updated. The refresh operation is taking longer than expected, impacting downstream reporting. Which of the following actions will likely NOT improve the performance of the materialized view refresh?

A. Adding more columns to the materialized view, even if they are not immediately required for reporting.
B. Analyzing the Query Profile of the Materialized View Refresh query to identify Bottlenecks and Optimization areas.
C. Ensuring the base table used by the materialized view has an appropriate clustering key.
D. Increasing the warehouse size used by the task executing the refresh.
E. Using the 'AUTO_REFRESH' property of the materialized view when appropriate and feasible.

正解：A

解説：
Adding more columns to the materialized view (Option C) will generally worsen the performance of refresh operations, as it increases the amount of data that needs to be processed and stored. The materialized view only needs the minimal set of columns required by its query. All other options will likely improve performance or provide data to improve performance. Option A allows for more resources to be used during the refresh. Option B reduces the amount of data that needs to be scanned. Option D automates the refresh process, making it more efficient overall and leverages Snowflake's automatic refresh optimization. Option E: by analyzing the query profile, data engineers can uncover specific areas for improvement within the query execution plan.

質問 # 135
You are working with a directory table named associated with an external stage containing a large number of small JSON files. You need to process only the files containing specific sensor readings based on a substring match within their filenames (e.g., files containing 'temperature' in the filename). You also want to load these files into a Snowflake table 'sensor_readings. Consider performance and cost-effectiveness. Which of the following approaches is the MOST efficient and cost-effective to achieve this? Choose TWO options.

A. Create a masking policy based on filenames to control which files users can see.
B. Use 'COPY INTO' with the 'PATTERN' parameter, constructing a regular expression that includes the substring match against the filename obtained from the directory table's 'relative_path' column.
C. Create a view on top of the directory table that filters the 'relative_patW based on the substring match, and then use 'COPY INTO' with the 'FILES' parameter to load the filtered files.
D. Load all files from the stage using 'COPY INTO' into a staging table, and then use a Snowflake task to filter and move the relevant records into the 'sensor_readingS table.
E. Use a Python UDF to iterate through the files listed in , filter based on filename, and then load each matching file individually using the Snowflake Python Connector.

正解：B、C

解説：
Options B and C are the most efficient and cost-effective. Option B (Create a view and use COPY INTO with FILES): Creating a view that filters the directory table allows you to isolate the relevant filenames. Then, using 'COPY INTO' with the 'FILES' parameter pointing to this filtered view directly instructs Snowflake to load only the specified files, minimizing unnecessary data processing. This is efficient as it leverages Snowflake's built-in capabilities. Option C (COPY INTO with the PATTERN parameter): The 'PATTERN' parameter within the 'COPY INTO' command allows you to specify a regular expression. By incorporating the substring match into this regular expression against the metadata$filename" , you can directly filter which files are loaded during the 'COPY INTO operation. This avoids loading irrelevant data and is generally more performant than iterating through files using a UDF. Other options are less efficient or less cost-effective: Option A (Python UDF): Using a Python UDF for this task is generally less efficient. Snowflake is designed to handle this processing natively, and using UDF can lead to performance overhead due to data serialization and deserialization between Snowflake and the UDF environment. Option D (Load all and filter later): Loading all files into a staging table and then filtering is wasteful. It increases data processing time and costs since you're loading unnecessary data. It's always better to filter data closer to the source if possible. Option E (Masking Policy): Masking policies are for security, not data transformation. They are applied at the query level to prevent users from seeing data, but do not help in efficiently processing only specific files.

質問 # 136
A large e-commerce company stores clickstream data in an AWS S3 bucket. The data is partitioned by date and consists of Parquet files. They need to analyze this data in Snowflake without physically moving it into Snowflake's internal storage. However, the data frequently changes, and they need to ensure queries reflect the latest updates to the files without significant latency. Which of the following approaches would be MOST suitable, considering cost, performance, and data freshness?

A. Create a standard external table directly on the S3 bucket. Refresh the external table metadata using SALTER EXTERNAL TABLE ... REFRESH' on a daily schedule.
B. Create a series of views on top of the S3 bucket using 'READ_PARQUET function, updating view definitions whenever underlying files change.
C. Create an external table using a Snowflake-managed catalog. Configure a Snowpipe to automatically refresh the metadata as new files are added to the S3 bucket.
D. Create a standard external table with the 'AUTO REFRESH' parameter set to 'TRUE'. This will automatically refresh the metadata whenever changes are detected in S3.
E. Create an Iceberg table backed by the S3 bucket. Snowflake will automatically manage the metadata and handle incremental updates efficiently.

正解：E

解説：
Iceberg tables are the most suitable option in this scenario. They address the limitations of standard external tables regarding metadata management and incremental updates. 'AUTO REFRESH' for external tables isn't ideal for frequent changes as it still relies on scanning the file system. A Snowflake-managed catalog with Snowpipe could work, but Iceberg is more purpose-built for handling the data evolution in place. READ_PARQUET in views is inefficient as it requires parsing the data every time the view is queried. Iceberg provides ACID properties and optimized query performance on data residing in external storage. The key benefit is the automatic metadata management provided by Snowflake for Iceberg tables.

質問 # 137
You have a table 'EMPLOYEE DATA' containing Personally Identifiable Information (PII), including 'salary' and 'email'. You need to implement column-level security such that: 1) The 'salary' column is only visible to users in the 'FINANCE ROLE. 2) The 'email' column is masked with a SHA256 hash for all users except those in the 'HR ROLE. You create the following masking policies:

Which of the following SQL statements correctly applies these masking policies to the 'EMPLOYEE DATA table?

A. ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask_salary ON COLUMN salary; ALTER TABLE EMPLOYEE_DATAAPPLY MASKING POLICY mask email ON COLUMN email;
B. ALTER TABLE EMPLOYEE_DATAALTER COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATAALTER COLUMN email SET MASKING POLICY mask email;
C. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY mask email;
D. CREATE OR REPLACE TAG employee_data.salary VALUE 'mask_salary'; CREATE OR REPLACE TAG employee_data.email VALUE 'mask_email';
E. ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN salary SET MASKING POLICY = mask_salary; ALTER TABLE EMPLOYEE_DATA MODIFY COLUMN email SET MASKING POLICY = mask email;

正解：B

解説：
The correct syntax for applying a masking policy to a column is 'ALTER TABLE ALTER COLUMN SET MASKING POLICY MODIFY COLUMN' is used for changing data types or constraints, not applying masking policies. The sign after 'SET MASKING POLICY is incorrect syntax. There's no 'APPLY MASKING POLICY command in Snowflake.

質問 # 138
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